Senior AI Product Manager (AI Builder)

WEX•Washington, DC

5d•$125,300 - $154,100

About The Position

WEX is seeking a Senior AI Product Manager Builder to join a forward-deployed Builder Pod. This role involves working closely with an AI Tech Builder and embedded domain experts to transform manual, judgment-heavy workflows into governed, agent-assisted experiences. Unlike traditional PM roles, this position requires hands-on building, moving seamlessly from business workflow mapping to live prototyping, custom evaluations, and production monitoring. The AI PM Builder will own the problem, user outcome, business value case, agent behavior, and evaluation strategy, while the Tech Builder partner handles production engineering architecture and scalability. Key aspects of this role include building to learn by putting working concepts into users' hands early, prototyping using AI tools and low-code platforms, understanding agent behavior by inspecting prompts and outputs, writing and running evaluations, reimagining workflows for end-to-end outcomes, driving measurable business value, and building for scale and reuse. The role involves embedding with operators and domain experts to map workflows, define future-state experiences, and test feasibility. It also includes hands-on prototyping and agent design using WEX’s Agentic AI platform, pairing with a Tech Builder for development, and distinguishing between prototypes and production systems. Evaluation is a core discipline, requiring the definition of scenarios, criteria, and rubrics. Spec-driven development and forward deployment involve translating learnings into versioned artifacts and navigating enterprise systems. Responsible governance and value measurement require partnering with Risk, Compliance, Security, Legal, and AI Governance from day one, and instrumenting live product usage to connect agent performance to business ROI.

Requirements

7+ years of Product Management experience shipping software, data, platform, automation, or AI products to production at scale.
Applied AI Expertise: Direct experience with generative AI, LLMs, retrieval/grounding (RAG), tool calling, context management, orchestration, structured outputs, and human-in-the-loop workflows.
Evaluation Discipline: Proven experience building or running AI evaluations using golden datasets, quality rubrics, failure analysis, or model-based judges.
Prototyping & Technical Literacy: Demonstrated ability to use AI-assisted dev tools, low-code frameworks, APIs, and version-controlled configuration files (Markdown, JSON, YAML) to bring concepts to life.
Systems & Tradeoff Thinking: Skill in balancing accuracy, autonomy, latency, token/system cost, security, and user experience, with a clear understanding of when to use probabilistic AI versus deterministic code.
Communication & Collaboration: Track record of driving alignment across cross-functional partners (Engineers, Risk, Legal, Operations) and making complex AI tradeoffs clear to executive audiences.
Bachelor’s degree in a related field or equivalent practical experience.

Nice To Haves

Experience in highly regulated industries such as Fintech, Payments, Health/Benefits, Fleet, or Financial Services.
Background working with enterprise AI governance frameworks, model risk management, responsible AI, or auditability.
Hands-on experience with agent frameworks, prompt/context engineering tools, or AI experimentation platforms.

Responsibilities

Embed directly with operators, customers, and domain experts across Mobility, Payments, and Benefits to map actual current-state workflows, friction, and failure demand.
Define future-state experiences, set baseline performance metrics (cycle time, manual hours, containment, error rates), and select the smallest high-value slice to test feasibility and trust.
Rapidly build and test agent behavior, system context, prompts, tool flows, retrieval/grounding, and multi-agent or human-in-the-loop orchestrations using WEX’s Agentic AI platform.
Pair directly with your Tech Builder to open repositories, adjust configurations, test scenarios, examine execution traces, and debug agent logic together.
Distinguish between rapid exploratory learning prototypes and production systems requiring engineering hardening.
Treat evaluation as a core product discipline by defining representative, edge-case, and adversarial scenarios alongside domain experts.
Establish pass/fail criteria, assertions, and scoring rubrics covering accuracy, tool selection, citation quality, policy adherence, hallucination rates, PII/PHI/PCI safety, and cost per outcome.
Analyze failed traces to build error taxonomies (distinguishing between prompt, context, retrieval, model, or UX issues) to prevent "demo-driven development."
Translate learnings into versioned repository artifacts (PRDs, acceptance criteria, decision logic, eval harnesses) that serve as a shared source of truth.
Maintain clear traceability: User Problem → Workflow → Requirement → Scenario → Eval → Production Metric.
Navigate enterprise legacy systems, fragmented APIs, and complex operational policies to show working software in real environments.
Partner with Risk, Compliance, Security, Legal, and AI Governance from day one to embed decision boundaries, entitlements, auditability, prompt-injection defenses, and fallback controls.
Instrument live product usage to connect agent performance directly to top-line and bottom-line business ROI.
Package reusable patterns, runbooks, and controls so domain teams can seamlessly operate and extend capabilities as solutions mature.